Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 7 | Year 2026 | Article Id. IJECE-V13I7P108 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I7P108

An Iot-Integrated Federated Edge Computing Paradigm for Privacy-Preserving Smart Campus Infrastructure

C. Ravi Shankar Reddy, S. Bhargav, G. Thirupathi, K. Ramesh Babu

Received Revised Accepted Published
16 Mar 2026 21 Apr 2026 17 Jun 2026 29 Jul 2026

Citation :

C. Ravi Shankar Reddy, S. Bhargav, G. Thirupathi, K. Ramesh Babu, "An Iot-Integrated Federated Edge Computing Paradigm for Privacy-Preserving Smart Campus Infrastructure," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 117-126, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P108

Abstract

The rise in the use of the Internet of Things is resulting in extensive and varied data being generated on university campuses, especially in the domain of energy consumption and management in buildings. Conventional learning algorithms will fail in this scenario because they are restricted by privacy boundaries and communication cost considerations on edge devices. Against this background, we introduce a federated learning framework on campus. In which learning will occur at the edge nodes collaboratively. This scheme provides a resource-conscious multi-layer federating strategy that controls the number of devices taking part in the process as well as the frequency at which aggregation is done based on each device’s capability. Contrary to existing federating designs that assume each edge node is similar in ability, this design considers the differences in each campus environment. The experiments conducted using the realistic smart campus test bed reveal that our approach ensures stronger convergence, reduced communication overhead, as well as more accurate predictions of energy consumption, when compared to traditional Federated Averaging as well as the fixed aggregation strategy. Moreover, the approach allows for simplicity in the preservation of privacy, without losing scalability, especially for the smart campus setting that has been projected to be rather large.

Keywords

Edge computing, Energy Management Systems, Federated Learning, Internet of Things (IoT), Machine Learning, Smart campus, Privacy-Preserving.

References

  1. Jakub Konečný et al., “Federated Optimization: Distributed Machine Learning for On-Device İntelligence,” arXiv preprint, pp. 1-38, 2016.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. Brendan McMahan et al., “Communication-Efficient Learning of Deep Networks from Decentralized Data,” Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54, pp. 1273-1282, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. V. Smith et al., “Federated Multi-Task Learning,” Advances in NeuralInformation Processing Systems, vol. 30, pp. 4424-4434, 2017.
    [    Google Scholar] [Publisher Link]
  4. Keith Bonawitz et al., “Practical Secure Aggregation for Privacy-Preserving Machine Learning,” Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175-1191, 2017.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Qiang Yang et al., “Federated Machine Learning: Concept and Applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1-19, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Sebastian Caldas et al.,  “LEAF: A Benchmark for Federated Settings,” arXiv preprint, pp. 1-9, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Keith Bonawitz et al., “Towards Federated Learning at Scale: System Design,” Proceedings of Machine Learning and Systems, vol. 1, 2019.
    [    Google Scholar] [Publisher Link]
  8. Qinbin Li et al., “A Survey on Federated Learning Systems: Vision, Hype and Reality,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 4, pp. 3347-3366, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Shiqiang Wang et al., “Adaptive Federated Learning in Resource Constrained Edge Computing Systems,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1205-1221, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. Xiaofei Wang et al., “In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning,” IEEE Network, vol. 33, no. 5, pp. 156-165, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Nguyen H. Tran et al., “Federated Learning Over Wireless Networks: Optimization Model Design and Analysis,” IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, Paris, France, pp. 1387-1395, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Tian Li et al., “Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50-60, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Tian Li et al., “Federated Optimization in Heterogeneous Networks,” Proceedings of Machine Learning and Systems, vol. 2, 2020.
    [    Google Scholar] [Publisher Link]
  14. Sai Praneeth Karimireddy et al., “SCAFFOLD: Stochastic Controlled Averaging for Federated Learning,” Proceedings of the 37th International Conference on Machine Learning, vol. 119, pp. 5132-5143, 2020.
    [    Google Scholar] [Publisher Link]
  15. Xiang Li et al., “On the Convergence of FedAvg on Non-IID Data,” arXiv preprint, pp. 1-26, 2020.
    [    Google Scholar] [Publisher Link]
  16. Mohammed Aledhari et al., “Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Access, vol. 8, pp. 140699-140725, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. Seyyedali Hosseinalipour et al., “From Federated to Fog  Learning: Distributed Machine Learning over Heterogeneous Wireless Networks,” IEEE Communications Magazine, vol. 58, no. 12, pp. 41-47, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Tuo Zhang et al., “Federated Learning for Internet of Things: Applications, Challenges, and Opportunities,” IEEE Internet Things Magagine, vol. 5, no. 1, pp. 24-29, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Wei Yang Bryan Lim et al., “Federated Learning in Mobile Edge Networks: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 2031-2063, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Jie Xu et al., “Federated Learning for Healthcare İnformatics,” Journal of Healthcare Informatics Research, vol. 5, no. 1, pp. 1-19, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Yang Zhao et al., “Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1817-1829, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. Frank Po-Chen Lin et al., “Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations,” IEEE Journal on Selected  Areas in Communications, vol. 39, no. 12, pp. 3851-3869, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Nir Shlezinger et al., “UVeQFed: Universal Vector Quantization for Federated Learning,” IEEE Transactions on Signal Processing, vol. 69, pp. 500-514, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Zhiyuan Wang et al., “Resource-Efficient Federated Learning With Hierarchical Aggregation in Edge Computing,” IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, Vancouver, BC, Canada, pp. 1-10, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Fan Lai et al., “FedScale: Benchmarking Model and System Performance of Federated Learning at Scale,” Proceedings of the 39th International Conference on Machine Learning, vol. 162, pp. 11814-11827, 2022.
    [    Google Scholar] [Publisher Link]
  26. Chong Yu et al., “Secure and Efficient Federated Learning Against Model Poisoning Attacks in Horizontal and Vertical Data Partitioning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 6, pp. 10913-10927, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Heiko Ludwig, and Nathalie Baracaldo, Federated  Learning: A Comprehensive Overview of Methods and Applications, Springer, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Tuo Zhang et al., “Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities,” IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 24-29, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Sawsan Abdulrahman et al., “A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond,” IEEE Internet Things Journal, vol. 8, no. 7, pp. 5476-5497, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Di Chai et al., “A Survey for Federated Learning Evaluations: Goals and Measures,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 10, pp. 5007-5024, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Adeb Salh et al., “Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G,” IEEE Access, vol. 11, pp. 16353-16367, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  32. Xiaodi Ma et al., “Federated Learning and Resource-Aware Graph Neural Network for İntrusion Detection in 6G-IoT Driven Healthcare System,” IEEE Internet of Things Journal, vol. 13, no. 5, pp. 7749-7761, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. Sangyoon Lee, Le Xie, and Dae-Hyun Choi, “Privacy-Preserving Energy Management of a Shared Energy Storage System for Smart Buildings: A Federated Deep Reinforcement Learning Approach,” Sensors, vol. 21, no. 14, pp. 1-21, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  34. Ninghui Jia et al., “A Comprehensive Survey on Communication-Efficient Federated Learning in Mobile Edge Environments,” IEEE Communications Surveys & Tutorials, vol. 27, no. 6, pp. 3710-3741, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  35. Xingfu Yi et al., “RHFedMTL: Resource-Aware Hierarchical Federated Multitask Learning,” IEEE Internet of Things Journal, vol. 11, no. 14, pp. 25227-25237, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]